Backward Semantic for improving Multi-hop Question Answering on Knowledge Graph

Anonymous

Abstract: Multi-hop question answering over knowledge graph utilizes the knowledge graph (KG) structure to infer answers. However, KG often lacks edges in the reasoning path from the question entity to the answer entity. Recent research focused on various KG embedding methods to obtain the semantics of the reasoning path (called forward semantics) to repair missing edges. However, the forward semantics method could drift as the path get longer. This paper proposes a bidirectional semantics embedding and matching method (BSEM) to alleviate the forward semantics drift problem. BSEM first leverages a backward semantics method to deduce the semantics of the opposite direction of the reasoning path. Then, BSEM constructs a two-stage learning method to merge the bidirectional (forward or backward) semantics and find the correct answer. In the two-stage learning method, joint learning is created to learn the bidirectional semantics of the reasoning path simultaneously; contrast learning is also used to improve the ability of the backward semantics to identify the correct answers that are not found by the forward semantics. Experiments on the two benchmarks, MetaQA and WebQSP, show that BSEM surpasses the five baseline methods, PullNet, EmQL, LEGO, EmbedKGQA, and KGT5. Especially for the incomplete KG -- WebQSP, compared with the other four methods except for EmQL, BSEM improves the accuracy by 13.1%, 12.0%, 5.2% and 10.0%, respectively.

Paper Type: long

Research Area: Question Answering